import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules

df = pd.read_excel('./associationRules/餐厅数据.xlsx')
print(df.head)

#转换菜品格式
trsations = df['菜品'].str.split(',').to_list()

te = TransactionEncoder()
te_ary = te.fit(trsations).transform(trsations)
df_enecoded = pd.DataFrame(te_ary, columns=te.columns_);

#使用apriori进行分析

frequent_itemsets = apriori(df_enecoded, min_support=0.1, use_colnames=True)

frequent_itemsets.sort_values(by='support', ascending=False, inplace=True)

#选择2项集
print(frequent_itemsets[frequent_itemsets.itemsets.apply(lambda x : len(x)== 2)])

#生成关联规则
rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.1)

#筛选有效规则
effective = rules[
    (rules['lift']>1)&(rules['confidence']>0.1)
].sort_values(by=['lift','confidence'], ascending=False)

print(effective[['antecedents','consequents','support','confidence','lift']])

import matplotlib.pyplot as plt
import networkx as nx
#设置字体
plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False
#关联可视化
G = nx.DiGraph()

for _, row in effective.iterrows():
    G.add_edge(','.join(list(row['antecedents'])),
               ','.join(list(row['consequents'])),
               weight=row['lift'])

plt.figure(figsize =(12, 8))

pos = nx.spring_layout(G)
nx.draw(G, pos,
        with_labels=True,
        edge_color=[G[u][v]['weight'] for u,v in G.edges()],
        width=2.0,
        edge_cmap=plt.cm.Blues)
plt.show()